The present invention relates to a control system for controlling the motion of an object, and is particularly suitable for controlling a moving robot.
In controlling an object to move from state A to state B, a PID control method has been used generally whereby a target value changing with time during the course from state A to state B is given externally, and a difference from the target value is controlled so as to become small. In practice, however, it is difficult to find and externally give an ideal state transition if the motion is complicated. For example, consider the walk control of a moving robot. An optimum next motion cannot be determined unless taking into consideration various information such as the condition of a floor, the figure of feet and arms, the posture of the robot, the position of the robot's center of gravity, and the output values from many contact sensors. Even if one of the newest presently available computers is used, it is difficult to make a program for such walk control.
In order to solve the above problem, a study of automatically determining the motion of a robot through learning is now being made. An approach to optimize the control of a walking robot through learning is reported in a document "System for Self-forming Motion Pattern" by Nakano, and Douyani, 23th SICE Scientific Lecture Preliminary Papers S1-3, July 1984. According to this paper, while a cyclic motion is carried out by a walking robot having two articulations, a target or goal to make maximum an average progressing distance per cycle is provided to thereby optimize the parameters of cyclic motion on the trial-and-error basis. In this way, the robot which cannot move properly at first, gradually moves quickly and smoothly.
Another example having a learning function is a manipulator control as described in a document "Manipulator Control by Inverse-dynamics Model Learned in Multi-layer Neural Network" by Setoyama, Kawato and Suzuki, The Institute of Electronics, Information and Communication Engineers of Japan, ME and BIO-Sybernetics, Technical Report MBE87-135. According to this report, a multi-layer neural network is built in a control system, and a feed-forward type control circuit is formed while making minimum a shift from a target value through learning. In this way, the motion of the manipulator is optimized by gradually reducing a delay from the target value during its motion.
The above-described conventional techniques herein incorporated with reference to the above documents, positively uses a learning function so that there is a possibility of greatly simplifying the programs for a complicated control system. With the conventional technique, however, the parameters of fundamental motions only are learned. Accordingly, the fundamental motions must be first preset. It may become, therefore, difficult to control a complicated motion of a robot in some cases.